From Scattered Decisions to Analytical Governance: KMDS Advances

Machine learning failures often stem from undocumented design decisions that disappear into notebooks and individual memories. KMDS addresses this by establishing structured metadata understanding, feature engineering guidance, and design governance within projects. The framework transforms scattered analytical decisions into reusable assets, enabling organizations to preserve and query their entire ML decision-making lifecycle.